Manolis Kellis

TL;DR: This paper introduces a method called Network Infusion (NI), designed to circumvent issues of source inference practical for large, complex real world networks, and proposes an integrative source inference framework that combines NI with a distance centrality-based method, which leads to a robust performance in cases where the underlying dynamics are unknown.

TL;DR: This paper performed single-cell characterization of the human cerebrovasculature using both ex vivo fresh-tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples.

TL;DR: This work uses epigenomic annotations across 127 tissues and cell types to investigate weak regulatory associations, the specific enhancers they reside in, their downstream target genes, their upstream regulators, and the biological pathways they disrupt in eight common diseases.

TL;DR: Causal Multivariate Mediation within Extended Linkage disequilibrium is developed, a novel Bayesian inference framework to jointly model multiple mediated and unmediated effects relying only on summary statistics, and it is shown in simulation that CaMMEL accurately distinguishes between mediating and pleiotropic genes unlike existing methods.

TL;DR: A new Bayesian model RiVIERA-beta (Risk Variant Inference using Epigenomic Reference Annotations) is introduced for inference of driver variants by modelling summary statistics p-values in Beta density function across multiple traits using hundreds of epigenomic annotations to model GWAS summary statistics of 9 autoimmune diseases and Schizophrenia.